预测锂离子电池循环寿命的自适应数据驱动方法

IF 22.7 1区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Infomat Pub Date : 2024-01-07 DOI:10.1002/inf2.12521
Chao Han, Yu-Chen Gao, Xiang Chen, Xinyan Liu, Nan Yao, Legeng Yu, Long Kong, Qiang Zhang
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引用次数: 0

摘要

利用早期循环数据准确预测锂离子电池(LIB)的非线性退化,可以明显缩短电池测试时间,从而加快电池优化和生产。本研究提出了一种自适应长短期记忆(SA-LSTM)方法,仅利用早期循环数据预测电池退化轨迹和电池寿命。具体来说,通过基于时间序列的方法从放电电压曲线中提取了两个特征,并利用 SA-LSTM 模型对进一步的循环进行了预测。获得的特征与容量相关联,通过广义多元线性回归模型预测容量衰减轨迹。当使用早期循环放电信息直至 90% 容量保持率时,所提出的方法对放电容量和寿命终止的平均在线预测误差分别为 6.00% 和 6.74%。此外,通过将特征与每个周期的平均温度相关联,突出了温度控制的重要性。这项工作开发了一种自适应数据驱动方法,可准确预测锂电池的循环寿命,并揭示了潜在的降解机制和控制环境温度的重要性。
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A self-adaptive, data-driven method to predict the cycling life of lithium-ion batteries

Accurately forecasting the nonlinear degradation of lithium-ion batteries (LIBs) using early-cycle data can obviously shorten the battery test time, which accelerates battery optimization and production. In this work, a self-adaptive long short-term memory (SA-LSTM) method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data. Specifically, two features were extracted from discharge voltage curves by a time-series-based approach and forecasted to further cycles using SA-LSTM model. The as-obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model. The proposed method achieved an average online prediction error of 6.00% and 6.74% for discharge capacity and end of life, respectively, when using the early-cycle discharge information until 90% capacity retention. Furthermore, the importance of temperature control was highlighted by correlating the features with the average temperature in each cycle. This work develops a self-adaptive data-driven method to accurately predict the cycling life of LIBs, and unveils the underlying degradation mechanism and the importance of controlling environmental temperature.

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来源期刊
Infomat
Infomat MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
37.70
自引率
3.10%
发文量
111
审稿时长
8 weeks
期刊介绍: InfoMat, an interdisciplinary and open-access journal, caters to the growing scientific interest in novel materials with unique electrical, optical, and magnetic properties, focusing on their applications in the rapid advancement of information technology. The journal serves as a high-quality platform for researchers across diverse scientific areas to share their findings, critical opinions, and foster collaboration between the materials science and information technology communities.
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